Tian Shi

Orcid: 0009-0003-0001-9617

Affiliations:
  • Southeast University, Nanjing, Jiangsu, China


According to our database1, Tian Shi authored at least 13 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Modern Compliant Robot Control: Exploring Benefits From Singular Perturbation Synthesis.
IEEE Trans. Ind. Electron., March, 2025

2024
Comparative Analysis of Parameter Convergence for Several Least-Squares Estimation Schemes.
IEEE Trans. Autom. Control., May, 2024

Composite Learning Variable Impedance Robot Control With Stability and Passivity Guarantees.
IEEE Robotics Autom. Lett., January, 2024

Adaptive Estimation and Control With Online Data Memory: A Historical Perspective.
IEEE Control. Syst. Lett., 2024

Composite learning backstepping control with guaranteed exponential stability and robustness.
CoRR, 2024

Composite Learning Cartesian Impedance Control Under Uncertain Robot Dynamics.
Proceedings of the 22nd IEEE International Conference on Industrial Informatics, 2024

Gaussian Process-Enhanced Impedance Iterative Learning for Robot Interaction Control.
Proceedings of the IEEE Conference on Artificial Intelligence, 2024

Efficient Composite Learning Robot Control Under Partial Interval Excitation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Memory Regressor Extended Echo State Networks for Nonlinear Dynamics Modeling.
Proceedings of the European Control Conference, 2024

Composite Learning Exponential Parameter Estimation for Discrete-Time Nonlinear Systems.
Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

2023
Acceleration-Free Recursive Composite Learning Control of High-DoF Robot Manipulators.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Least-Squares Composite Learning Backstepping Control with High-Order Tuners.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
Reinforcement Learning-Based Impedance Learning for Robot Admittance Control in Industrial Assembly.
Proceedings of the International Conference on Advanced Robotics and Mechatronics , 2022


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